Modeling the establishment of invasive species: habitat and biotic interactions influencing the establishment of Bythotrephes longimanus

Abstract

Bythotrepheslongimanus is an invasive pelagic crustacean, which first arrived in North America from Europe in early 1980s and can now be found throughout the Great Lakes and in many inland lakes and waterways. Determining the suitability of lakes to Bythotrephes establishment is an important step in quantifying its potential habitat range and environmental risk. Lake environmental conditions, planktivorous fishes, sport fishes and Bythotrephes occurrence data from 179 south-central Ontario lakes were used in this study to model lake characteristics suitable for its establishment. The performance of principal component analysis and different predictive models was used to determine the habitats that are suitable for the survival of Bythotrephes and the factors that may regulate its spread. Four modeling approaches were employed: linear discriminant analysis; multiple logistic regression; random forests; and, artificial neural networks. Ensemble prediction based on the four modeling approaches was also used as an indicator for predicting Bythotrephes occurrence. Bythotrephes appears to establish more readily in larger, deeper lakes with lower elevation, that have more sport fishes. Bythotrephes occurrence can be best predicted by artificial neural networks when including the measures of fish data, in addition to lake environmental data. Lake elevation, surface area and sport fish occurrence were ranked as the most important predictors of Bythotrephes invasion. The inclusion of biotic variables (occurrence or diversity of sport or planktivorous fishes) enhanced cross-validated models relative to analyses based on environmental data alone.

Notes

Acknowledgments

We would like to thank NSERC, CAISN and the various research funding partners for facilitating this research. We thank Norman Yan for leading the field sampling program, for providing the data, and for comments on earlier presentations of this work. We also thank the Ontario Ministry of Natural Resources for providing fish composition data.

Phi coefficient provides a measure of how well the predictions from each model match one another for predictions at the level of individual lakes. Abbreviations for models are: LDA linear discriminant analysis, MLR multiple logisitic regression, RF random forests, ANN artificial neural networks